Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Telugu
whisper
Eval Results (legacy)
Instructions to use bhavik2026/whisper-small-te with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhavik2026/whisper-small-te with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bhavik2026/whisper-small-te")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bhavik2026/whisper-small-te") model = AutoModelForSpeechSeq2Seq.from_pretrained("bhavik2026/whisper-small-te") - Notebooks
- Google Colab
- Kaggle
Finetuned openai/whisper-small on 99 Telugu training audio samples from ./cv-corpus-26.0-2026-06-12/te.
This model was created from the Mozilla.ai Blueprint: speech-to-text-finetune.
Evaluation results on 93 audio samples of Telugu:
Baseline model (before finetuning) on Telugu
- Word Error Rate (Normalized): 211.709
- Word Error Rate (Orthographic): 197.708
- Character Error Rate (Normalized): 174.02
- Character Error Rate (Orthographic): 229.888
- Loss: 2.799
Finetuned model (after finetuning) on Telugu
- Word Error Rate (Normalized): 77.809
- Word Error Rate (Orthographic): 102.006
- Character Error Rate (Normalized): 47.658
- Character Error Rate (Orthographic): 64.089
- Loss: 1.078
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Model tree for bhavik2026/whisper-small-te
Base model
openai/whisper-smallEvaluation results
- wer on Common Voice (Telugu)self-reported77.809